18F-FDG PET/CT of Intracholecystic Papillary Neoplasm in Cystic Neck and Duct.

ABSTRACT: We report 18F-FDG PET/CT appearances of intracholecystic papillary neoplasm (ICPN) in the gallbladder neck and duct of a 74-year-old woman with a history of hepatitis B cirrhosis. The lesion presented with a large and sessile soft mass in the neck and duct of gallbladder with obvious glucose metabolism on PET/CT images, which was confirmed pathologically as ICPN (gastric foveolar type) with high-grade intraepithelial neoplasia. ICPN localized in the gallbladder neck and duct is extremely rare, and is easily misdiagnosed as gallbladder carcinoma. Our report aids in the application of PET/CT in the differential diagnosis of ICPN and guiding early surgery.

Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning.

PURPOSE: Microvascular invasion (MVI) is a valuable predictor of survival in hepatocellular carcinoma (HCC) patients. This study developed predictive models using eXtreme Gradient Boosting (XGBoost) and deep learning based on CT images to predict MVI preoperatively. METHODS: In total, 405 patients were included. A total of 7302 radiomic features and 17 radiological features were extracted by a radiomics feature extraction package and radiologists, respectively. We developed a XGBoost model based on radiomics features, radiological features and clinical variables and a three-dimensional convolutional neural network (3D-CNN) to predict MVI status. Next, we compared the efficacy of the two models. RESULTS: Of the 405 patients, 220 (54.3%) were MVI positive, and 185 (45.7%) were MVI negative. The areas under the receiver operating characteristic curves (AUROCs) of the Radiomics-Radiological-Clinical (RRC) Model and 3D-CNN Model in the training set were 0.952 (95% confidence interval (CI) 0.923-0.973) and 0.980 (95% CI 0.959-0.993), respectively (p = 0.14). The AUROCs of the RRC Model and 3D-CNN Model in the validation set were 0.887 (95% CI 0.797-0.947) and 0.906 (95% CI 0.821-0.960), respectively (p = 0.83). Based on the MVI status predicted by the RRC and 3D-CNN Models, the mean recurrence-free survival (RFS) was significantly better in the predicted MVI-negative group than that in the predicted MVI-positive group (RRC Model: 69.95 vs. 24.80 months, p < 0.001; 3D-CNN Model: 64.06 vs. 31.05 months, p = 0.027). CONCLUSION: The RRC Model and 3D-CNN models showed considerable efficacy in identifying MVI preoperatively. These machine learning models may facilitate decision-making in HCC treatment but requires further validation.

Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography.

BACKGROUND: The accurate classification of focal liver lesions (FLLs) is essential to properly guide treatment options and predict prognosis. Dynamic contrast-enhanced computed tomography (DCE-CT) is still the cornerstone in the exact classification of FLLs due to its noninvasive nature, high scanning speed, and high-density resolution. Since their recent development, convolutional neural network-based deep learning techniques has been recognized to have high potential for image recognition tasks. AIM: To develop and evaluate an automated multiphase convolutional dense network (MP-CDN) to classify FLLs on multiphase CT. METHODS: A total of 517 FLLs scanned on a 320-detector CT scanner using a four-phase DCE-CT imaging protocol (including precontrast phase, arterial phase, portal venous phase, and delayed phase) from 2012 to 2017 were retrospectively enrolled. FLLs were classified into four categories: Category A, hepatocellular carcinoma (HCC); category B, liver metastases; category C, benign non-inflammatory FLLs including hemangiomas, focal nodular hyperplasias and adenomas; and category D, hepatic abscesses. Each category was split into a training set and test set in an approximate 8:2 ratio. An MP-CDN classifier with a sequential input of the four-phase CT images was developed to automatically classify FLLs. The classification performance of the model was evaluated on the test set; the accuracy and specificity were calculated from the confusion matrix, and the area under the receiver operating characteristic curve (AUC) was calculated from the SoftMax probability outputted from the last layer of the MP-CDN. RESULTS: A total of 410 FLLs were used for training and 107 FLLs were used for testing. The mean classification accuracy of the test set was 81.3% (87/107). The accuracy/specificity of distinguishing each category from the others were 0.916/0.964, 0.925/0.905, 0.860/0.918, and 0.925/0.963 for HCC, metastases, benign non-inflammatory FLLs, and abscesses on the test set, respectively. The AUC (95% confidence interval) for differentiating each category from the others was 0.92 (0.837-0.992), 0.99 (0.967-1.00), 0.88 (0.795-0.955) and 0.96 (0.914-0.996) for HCC, metastases, benign non-inflammatory FLLs, and abscesses on the test set, respectively. CONCLUSION: MP-CDN accurately classified FLLs detected on four-phase CT as HCC, metastases, benign non-inflammatory FLLs and hepatic abscesses and may assist radiologists in identifying the different types of FLLs.

Deep learning assisted differentiation of hepatocellular carcinoma from focal liver lesions: choice of four-phase and three-phase CT imaging protocol.

PURPOSE: To evaluate whether a three-phase dynamic contrast-enhanced CT protocol, when combined with a deep learning model, has similar accuracy in differentiating hepatocellular carcinoma (HCC) from other focal liver lesions (FLLs) compared with a four-phase protocol. METHODS: Three hundred and forty-two patients (mean age 49.1 ± 10.5 years, range 19-86 years, 65.8% male) scanned with a four-phase CT protocol (precontrast, arterial, portal-venous and delayed phases) were retrospectively enrolled. A total of 449 FLLs were categorized into HCC and non-HCC groups based on the best available reference standard. Three convolutional dense networks (CDNs) with the input of four-phase CT images (model A), three-phase images without portal-venous phase (model B) and three-phase images without precontrast phase (model C) were trained on 80% of lesions and evaluated in the other 20% by receiver operating characteristics (ROC) and confusion matrix analysis. The DeLong test was performed to compare the areas under the ROC curves (AUCs) of A with B, B with C, and A with C. RESULTS: The diagnostic accuracy in differentiating HCC from other FLLs on test sets was 83.3% for model A, 81.1% for model B and 85.6% for model C, and the AUCs were 0.925, 0.862 and 0.920, respectively. The AUCs of models A and C did not differ significantly (p = 0.765), but the AUCs of models A and B (p = 0.038) and of models B and C (p = 0.028) did. CONCLUSIONS: When combined with a CDN, a three-phase CT protocol without precontrast showed similar diagnostic accuracy as a four-phase protocol in differentiating HCC from other FLLs, suggesting that the multiphase CT protocol for HCC diagnosis might be optimized by removing the precontrast phase to reduce radiation dose.

The Clinical Significance of miR-135b-5p and Its Role in the Proliferation and Apoptosis of Hippocampus Neurons in Children with Temporal Lobe Epilepsy.

Temporal lobe epilepsy (TLE) is the most familiar localized epilepsy in children. MicroRNAs (miRNAs) are essential for the inhibition or promotion of numerous diseases. This study aimed to detect the expression of miR-135b-5p and primarily uncover its underlying function and mechanism in children with TLE. Quantitative real-time polymerase chain reaction was used to evaluate the expression of miR-135b-5p in children with TLE and in a rat model of epilepsy. MTT assay and flow cytometric apoptosis assay were conducted to evaluate the effects of miR-135b-5p on cell viability and apoptosis. Additionally, the dual luciferase reporter assay was performed to confirm the direct target of miR-135b-5p. Our data showed that the expression of miR-135b-5p was significantly decreased in children with TLE and in the epileptic rat neuron model. The dysregulation of miR-135b-5p could serve as a promising diagnostic biomarker for children with TLE. The overexpression of miR-135b-5p moderated the adverse influence on cell viability and apoptosis induced by magnesium-free medium. SIRT1 was identified as a target gene of miR-135b-5p. These results proved that miR-135b-5p might serve as a potential diagnostic biomarker in children with TLE. Overexpression of miR-135b-5p alleviates the postepileptic influence on cell viability and apoptosis by targeting SIRT1.

Relationship between serotonin transporter gene polymorphism and constipation in cancer patients.

INTRODUCTION: To assess the potential association between serotonin transporter gene insertion/deletion polymorphism and the cancer-related constipation phenotype. MATERIAL AND METHODS: A total of 120 patients diagnosed with malignant solid tumors were subjected to genotyping. For the two groups - patients with constipation and constipation-free patients with non-gastrointestinal cancer, 60 cases in each group - we collected the peripheral venous blood. We extracted genomic DNA, and used polymerase chain reaction (PCR) to analyze the serotonin transporter (5-HT) link polymorphic region (5-HTTLPR) polymorphism of the serotonin transporter gene. RESULTS: The frequency of S/S genotype in cancer patients with constipation was 66.67% (40/60), and the frequency of the S allele was 79.17% (95/120); the frequency of S/S genotype in cancer patients without constipation was 48.33% (29/60), and the frequency of the S allele was 65.83% (79/120). There was a significant difference between the two groups (p < 0.05). CONCLUSIONS: The presence of 5-HTTLPRS/S genotype and the S allele in patients with cancers probably carry an increased risk of constipation. However, its role as a cause of cancer-related constipation needs to be further investigated.